When looking at all of the data available today, it is difficult not to get intimidated or frustrated. There are large, national data sources that collect and compile data. There are small, niche data sources that bring value to specific dimensions of the marketplace. Then, there are every size, shape, and type in between. There are so many options that companies can get lost or overwhelmed when assessing data sources for your specific business.

Partnering with data companies that already take all of the information mentioned above and analytically boil it down to the best and most predictive data can save you a lot of money. Revenue that currently was being eaten up paying for unneeded data sources and extra internal processing capabilities can now flow through to your bottom line.

Working with a data partner that already takes the time to understand the empirical value in each data source and then combines this information with business-based decisions from decades of experience will remove much of the stress that comes from the data acquisition process. The result is an optimized set of data that brings the best overall value to your business.

Econometric data is made up of thousands of variables that measure changes over time in the economic forces that affect all of our lives. These variables include changes in employment rates, retail sales, incomes, housing starts and completions, net migration, shopping center construction, etc. Generally at an area level (zip code, county, state, or metropolitan statistical areas), these variables allow an analyst to see how the economy is changing, and how those changes may affect peoples’ lives in the future.

At Analytics-IQ, we use many of these variables to look at quarterly changes at the MSA (“metropolitan statistical area”) level. Also called CBSA, these variables combine cities with the immediate areas around them, to give a full picture of the unique eco-system of a geographic area of the country. For example, the city of Atlanta has a population of just over 400,000 residents. The Atlanta MSA includes the 21 counties surrounding the city itself, since these outside counties are tied directly to the economic health of the city. Therefore, the Atlanta MSA has over 4 million residents. Typically, a county is included in the MSA if 20% or more of that county’s population commutes into the MSA on a daily basis for work or other economic activities.

Vector is an econometric product that AIQ has developed to look at economic changes and to compare those changes of the 350+ MSA’s in the United States. Each quarter, we measure the economic “health” of each of these MSA’s and compare the ones that are growing vs. the ones that are stagnant or falling behind the rest of the country. By comparing the changes in the local economies, we can see where economic growth is heading, and which MSA’s are in need of help.

Vector looks at the total economic health of an area, from growth in wages, migration, housing starts, housing sales, retail sales, bankruptcy rates, etc. It then assigns a Group (A through J) to each MSA, based on the strength of the local economy. MSA’s in Group A are the MSA’s that are showing the strongest growth and the most improving conditions for its residents. The MSA’s in Group J are the weakest economies and/or the economies that are falling behind, relative to the rest of the country.

Below are three examples of how Group A (the strongest growth MSA’s) and Group J (the weakest growth MSA’s) differ. As you can see, over time, Group A has shown bigger decreases in the unemployment rate and bigger increases in retail sales and housing prices.

Note the economic “shock” that occurred in late 2008. The economic recession that the U.S. experienced is blatant in these charts. As you can see, unemployment went through the roof, while retail sales plummeted, and housing prices dropped. Retail sales fell so low in 4Q08, that that was the first Christmas season since the 1950’s that Christmas sales actually FELL compared to the previous year. (Retailers always complain about Christmas sales being “down”, but they really mean that sales didn’t increase as much as they wanted/expected. In 2008, sales actually decreased.)

Note that since late 2009, retail sales growth for the MSA’s in Group A fluctuates around 2% each quarter. For the MSA’s in Group J, retail sales growth averages around 0% to 0.5% In that same time period, Group A has seen the unemployment rate drop about 5% per quarter, while Group J has ranged from a slight decrease to a sizable increase in unemployment. Again, the MSA’s that make up Groups A through J change each quarter, based on how each MSA is doing economically. Examining the overall Groups gives us a snapshot of the U.S. economy as a whole, but looking at individual MSA’s in-depth is also very important.

The nice thing about Vector is that it looks at such a wide range of variables when classifying into which of the 10 Groups each MSA belongs. Over time, MSA’s move up and down the ladder, based on how their growth (or decline) compares to the rest of the U.S. During the recession, MSA’s in the “sand” states (Florida, New Mexico, Arizona, California) were hit hard due to falling real estate prices. MSA’s in these states were consistently in Groups H, I, and J. Recently, however, these states have moved up, as real estate prices (a prime driver in economic health) have returned to earlier levels.

New Orleans, hit hard by Hurricane Katrina in 2005, showed strong growth for several years afterwards, thanks to the economic stimulus from government funding and a large rebuilding initiative spearheaded by both public and private sources. Recently, as those rebuilding projects have been completed and life for residents has returned to normal, the New Orleans MSA has slowly returned to growth patterns seen prior to the disaster.

The idea behind Vector is to identify which MSA’s are growing compared to the rest of the U.S., and where economic opportunities lie in the near future. We can use a wide range of economic data to pinpoint those areas where strong growth for businesses and individuals exist now, and will improve in the future.

What are the most troubling pitfalls of analytics? Here’s our countdown of the top 5 mistakes to avoid when using predictive analytics.

5. Thinking Too Big or Too Small

While your overall analytics goal may be to optimize profitability, it is important to predict the individual components that drive profitability (such as response and conversion). On the other hand, you do want to have some faith in the power of analytics, which can be used to predict medium sized things that add up to big business impacts.

4. Predicting The Past Instead of The Future

It is common to see models that perfectly predict what didhappen. Modeling techniques that extensively use historical data may seem great, but usually do not do a good job of predicting the future.

3. Making The Assumption That Analytics is a Pure Science

While ideal analysts often have a technical academic background, there’s actually an art to building effective statistical models. More important is an understanding of the business issues driving the need and then leveraging statistical tools to achieve the goals.

2. Data Bias

It’s very important to spend time evaluating the input data prior to executing the analysis. Here’s an important formula:

(bad data in)=(bad solution out)

1. Predicting The Wrong Thing

Real experience is needed to understand what dependent variable data is needed to reach a particular business goal. Pragmatism is almost always vital to success in the real world of messy data and fuzzy goals.

While we at AIQ do occasionally design ‘custom’ analytical projects (where we point our data at a dependent variable provided by a client), we are increasingly in the business of providing ‘embedded analytics’. What does this mean? It is the concept of making a ton of generic predictions:

How influential is an individual on Twitter?

When will a household make their next automobile purchase?

How much did a household’s net worth increase/decrease last year?

Of course, there is no way of knowing that someone is about to purchase a car… we can only predict this activity. Indeed, AIQ has dozens of such predictors that can increasingly meet our clients’ needs or provide great fuel for their analytics teams.

While we have focused much attention on building ’better mousetraps’ in terms of commodity data (things like age, income, gender and ethnicity), the real secret sauce in our data is our unique predictive fields.

1. Analytics versus Intuition for Precision Targeting.

Marketers often rely on their own human intuition to make targeting choices rather than leverage analytics to drive these important decisions. For example, marketers will often use past experience to estimate the selection of target variables and cutoffs for direct mail, email or digital campaigns.

While intuition can be a powerful tool in life, using it for marketing decisions can backfire as individual biases are then built into the decision process.

On the other hand, analytics rely on past behavior to statistically predict the future and have proven to be far more effective than intuition in establishing the right targets for marketing campaigns. Click here to learn more.

Household income is the most common selection criteria for consumer marketing. But studies show income may not be the best tool to predict consumer spend.

Our experts recommend the use of other affluence data, such as discretionary spend for better targeting. For example, 14% of the wealthiest US households are in the lowest 10% of spend while 16.5% of the lowest affluent households are in the highest 25% of spend. In this example, leveraging discretionary spend over household income alone is a more effective method of targeting prospects who are likely to buy. Click here to learn more.

3. An Emphasis on Offer and Targeting to Get to the Right Audience.

Studies show that creative execution drives only 20% of response while the right offer and target audience each deliver a whopping 40%.

As a marketer it makes sense to plan accordingly and spend enough time crafting the right offer to the right target market and then execute a creative strategy accordingly.

4. Getting to the Right Channel for the Prospect.

US consumers have many channel options today which provide marketers selling opportunities across digital, social, direct mail, email and mobile channels. We now know consumers have very specific channel preferences and experienced marketers will identify individual preferences and target their multi-channel campaigns accordingly. Identifying the right channel for the prospect and customer will result in increased response rates and greater engagement. Click here to learn more.

5. Tailoring the Right Content to the Right Prospect

Consumers are bombarded with hundreds, even thousands of marketing messages each day. Marketers know first impressions are significantly impactful which is why it’s important to immediately capture a prospect’s attention with a relevant offer and message.

Conducting the upfront research to really understand the prospect interests and preferences is a critical step towards getting it right. Highly predictive and market available analytics and segmentation systems are great tools to gain a more in-depth understanding of customers and prospects. Both help target the right content and improve the customer experience resulting in better campaign performance. Click here to learn more.

1. Using Intuition vs Analytics for Targeting

Despite the abundance of analytic resources available today, marketers often rely on their own human intuition to make important targeting choices vs allowing analytics to drive those decisions.

For example, when selecting targeting variables for a direct mail, email or digital campaign, marketers often guess when selecting targeting variables and cutoffs. While intuition can be a powerful tool in life, it often can backfire when making marketing decisions because our own biases are built into the decision process.

Analytics, on the other hand, uses past behavior to mathematically predict the future and has proven over the years much more effective than intuition. Click here to learn more.

2. Using Income vs Discretionary Spending to Target Potential Buyers.

Household income is the most common selection criteria for consumer marketing. But studies show it might not be the best tool to predict whether a consumer will open their wallet and spend.

Instead, try other affluence data for targeting like discretionary spend. For example, 14% of the wealthiest US households are in the lowest 10% of spending. Conversely, 16.5% of the lowest affluent households are in the highest 25% of spending.

In this example, leveraging discretionary spend would be more effective to target prospects likely to spend than just household income alone. Click here to learn more.

3. Spending Too Much Time on Creative Execution and Not Enough on Offer and Targeting.

Studies show that creative execution only drives 20% of response while offer and targeting the right audience is a whopping 40% each. So it makes sense to plan accordingly. Be sure to spend enough time crafting the right offer to the right target market and then execute creative accordingly.

4. Leveraging the Wrong Channel for the Prospect

US consumers have many channel options today. Digital, Social, Direct Mail, Email and now Mobile are all selling opportunities for marketers. We now know consumers have very specific channel preferences and smart marketers will learn these individual preferences and target their campaigns accordingly with a multi-channel approach. Leveraging the right channel for the prospect and customer will result in better marketing performance. Click here to learn more.

5. Not Tailoring the Right Content to the Right Prospect

Consumers are bombarded with hundreds, even thousands of marketing messages on a daily basis. We all know first impressions are powerful and that’s why it’s extremely important for marketers to capture their prospect’s attention immediately with a relevant offer and message. How? Do the upfront research to dig deep and really understand prospect interests, preferences and personas. Custom analytics and/or off the shelf segmentation systems are great tools to gain a more in depth understanding of customers and prospects. Both can help target the right content and improve overall marketing performance. Click here to learn more.

As is the case with many other marketing fields that are widely viewed as being ‘known’, ethnicity is actually an inferred field. While we cannot speak as to how other companies gather this data, we can state that this data is often wrong. We see numerous cases (in standard compiled data) where individuals are obviously misidentified. No doubt, anything can happen – but you should not see many cases of individuals identified as ‘Asian American’ having names such as ‘William Johnson’ and ‘Brandon Smith’.

Excluding the small number of ‘hand raisers’ on marketing files who have self-identified as a particular ethnicity, the primary characteristics used to infer ethnicity are:

US Census (2010)

First Name

Last Name

Religious affiliation (in some cases)

Purchase and behavioral data

It is important to note that none of the above characteristics are a silver bullet – large doses of analytics are needed to accurately predict ethnicity.

Does our approach work? An AIQ client with a large ‘truth’ file (customers who had self-identified their ethnic origin) compared our predictions and found that we were over 90% correct. Given that our ethnic universe is significantly larger (particularly in individuals that are predicted to have Hispanic or African-American ethnicity), this is a major achievement.

No ethnic predictor is perfect. AIQ can only claim that ours is a lot better.

Ethnic Group

AIQ%

African American

10.7%

Asian American (Chinese)

1.2%

Asian American (Indian)

0.7%

Asian American (Other)

1.5%

Caucasian (Jewish)

2.6%

Caucasian (Other)

64.3%

Hispanic

13.2%

Middle Eastern

1.1%

Native American/Pac Islander

0.4%

Other

0.0%

Unknown

4.4%

About Analytics-IQ, Inc.

Analytics-IQ (AIQ) is an Atlanta-based data and analytics company. AIQ was founded in 2007 to leverage analytics to create better marketing data products – in terms of accuracy and predictive power. AIQ’s data is used to make billions of marketing decisions every year.

To Learn More:

]]>http://analytics-iq.com/aiqs-new-ethnic-predictor/feed/0Adding Personal Beliefs in Marketing Modeling – Part 1: Religionhttp://analytics-iq.com/adding-personal-beliefs-in-marketing-modeling-part-1-religion/
http://analytics-iq.com/adding-personal-beliefs-in-marketing-modeling-part-1-religion/#commentsMon, 12 Jan 2015 15:44:48 +0000http://analytics-iq.com/?p=936]]>Marketing firms have been utilizing statistical modeling to improve their ability to identify potential customers for decades. Demographic data has been an integral part of this strategy, and companies have seen much success from the variables derived from this type of data. Census data is the center of most area-level modeling datasets, but there have been many additions over the years to the rich tapestry of available demographic data sources.

Why use Demographic Data?

Demographic data is favored by modelers because of its stability (nation-wide coverage, standardized variables), ease of use, and the simple fact that it works. Age, income, race, education, home ownership – these basic bits of information can tell a lot about an individual, a household, a zip4, or even a zip code. Put together, they can add up to a powerful tool in predicting potential customer behavior.

What are Marketers Missing?

One area involving potential customers, though, is often not used at all. This involves personal beliefs, dreams, and goals of individuals and communities. Religious beliefs, political leanings, charitable giving, and family status fall under this category. Demographic data tells us who someone is. Personal beliefs tell us who people choose to be.

Why is Personal Belief Data Missing?

The lack of use of personal belief data in most traditional modeling procedures is due to the difficulty of finding clean, uniform data that isn’t just local or regional, but can be applied to a national sample. In the past, these types of data have been harder to find, scrub, and interpret in a meaningful way. Many times, this data has been kept in paper form, and never compiled into any type of nationwide database. Fortunately, this is changing over time.

Taking a Look at Religious Beliefs Worldwide

Statistics show that the people of the world have a wide range of religious beliefs. According to Pew Research, the U.S. bares little in common with the world overall:

The U.S. population is much more Christian (Catholics + Protestants) than the rest of the world’s population, 78% vs. 33%. Also, most Eastern and Middle-eastern religions make up a tiny portion of the U.S. One interesting fact is that of the 14 million Jews in the world, 44% live in Israel, but 41% live in the U.S. The rest of the world has only the remaining 15% of the Jewish population.

Another important piece of information is that roughly half of all U.S. Protestants are actually official “members” of a Protestant church, while almost 80% of U.S. Catholics are “members” of a Catholic church. Membership and belief are not always correlated in religious data, something that can make using religious data challenging when modeling.

How to Find Religious Data

Religious data generally comes from two major sources – records of church memberships at the local (zip code and/or county) level and survey data of individuals. By tying these two sources together, it’s possible to create several valuable variables for modeling purposes. First, we can arrive at “adherent” variables, variables that measure the percentage of the local population who are actual members of various religions/denominations. Then, we can find the most common religion/denomination for different zip codes across the country, and which religions/denominations stand out as larger than their national footprint. The above map is from the Association of Statisticians of American Religious Bodies, and shows how different parts of the country favor different religions/denominations. Baptists and Catholics are the two largest religious groups in the country, for example, although in the Midwest, you’re just as likely to find Methodists and Lutherans in the (local) majority.

Once this information is distilled at the zip code level, it’s then possible to model religious preferences down to the zip4 level. This data can be incredibly useful when added to a model that uses traditional demographic data. The combination of demographics and religious beliefs can add valuable insights into identifying potential customers.

As an interesting aside, the largest groups in the U.S. are:

Baptist

Catholic

Lutheran

Methodist

Mormon

Church of Christ

Non-Denominational.

Conclusion

The above data can be used effectively for marketing purposes to add a new layer of understanding of a company’s customers (and potential customers). When folded into a solution that contains other, more traditional data fields, this type of belief data can give an added dimension to the population under review.

Future articles will look at how political beliefs and family lifestyles can also help in refining marketing efforts.

While difficult to quantify, most strategic marketers understand the value of receiving positive endorsements on social networks. The positives of ‘going viral’ can be tremendous – but how can a savvy marketer increase the odds of this form of free advertising? Are there privacy concerns involved with using social data without a consumer’s permission?

Issues of Privacy

The question regarding privacy is easy – if you have a national brand and you are accessing customer/prospect social accounts for the purpose of marketing products, you are absolutely taking a lot of risk related to negative PR and/or regulatory intervention. Do you really want to be featured in a coming expose for using consumer data without permission?

Solving the Private Data Issue

AIQ’s Social-IQ product suite is purely a predictive tool – it uses no private data in predicting whether a consumer is influential on Facebook or Twitter. That said, we have been able to validate the validity of the model: about 60% of the individuals we predict as being influential on Twitter have over 300 followers and have a minimum of 100 more followers than followed. In addition to the great power of these tools, we also can provide full coverage of the Facebook/Twitter universe.

For prospecting, we recommend using Social-IQ as a select in making campaign decisions – we can clearly eliminate about 20% of the universe that have virtually no chance of having influencers. If you are going to pay to acquire new accounts, why not acquire consumers who will evangelize your product or service to their online friends?

Feel free to contact Anne Smith to learn more about Social-IQ: (866) 612-4309.

“Symphony has been in development for years. We wanted to be 100% sure that we were bringing a segmentation tool to the market that was clearly better”, says Dave Kelly, CEO. “Symphony will allow our clients to achieve incremental gains in marketing results by pairing the most applicable content to each micro-segment. Our beta clients are seeing up to a 20% lift in results.”

Content optimization depends on the creation of truly homogeneous groups with which to test divergent content (i.e. creative, messages, subject lines, etc.). Symphony consists of 10 families (high-level segments) which are further divided into 90 micro-segments. “We were able to leverage AIQ’s thousands of proprietary data attributes to create the ultimate modern segmentation system, where each segment is homogeneous from a marketing perspective” says AIQ’s Chief Scientist, Gregg Weldon. “We also have been able to describe each segment using demographic, lifestyle, financial and social data.”

Symphony is now available for license and can be licensed directly from AIQ or from selected reseller partners.

About Analytics-IQ, Inc.

Analytics-IQ (AIQ) is an Atlanta-based data and analytics company. AIQ was founded in 2007 to leverage analytics to create better marketing data products – in terms of accuracy and predictive power. AIQ’s data is used to make billions of marketing decisions every year.